Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transfo...Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transformation.Making predictions for TTT diagrams of new steel rapidly and accurately is therefore of much practical importance,especially for costly and time-consuming experimental determination.Here,TTT diagrams for carbon and low-alloy steels were predicted using machine learning methods.Five commonly used machine learning(ML)algorithms,backpropagation artificial neural network(BP network),LibSVM,k-nearest neighbor,Bagging,and Random tree,were adopted to select appropriate models for the prediction.The results illustrate that Bagging is the optimal model for the prediction of pearlite transformation and bainite transformation,and BP network is the optimal model for martensite transformation.Finally,the ML framework composed of Bagging and BP network models was applied to predict the entire TTT diagram.Additionally,the ML models show superior performance on the prediction of testing samples than the commercial software JMatPro.展开更多
Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H...Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy during isothermal aging at temperatures between 450℃and 650℃for time to 75 h.The H-phase mean size and volume fraction were determined using transmission electron microscopy.Precipitation kinetics was analyzed using the Johnson-Mehl-Avrami-Kolmogorov equation and an Arrhenius type law.From these analyses,a Time-Temperature-Transformation diagram was constructed.The evolution of H-phase size suggests classical matrix diffusion limited Lifshitz-Slyozov-Wagner coarsening for all considered temperatures.The coarsening rate constants of H-phase precipitation have been determined using a modified coarsening rate equation for nondilute solutions.Critical size of H-phase precipitates for breaking down the precipitate/matrix interface coherency was estimated through a combination of age hardening and precipitate size evolution data.Moreover,time-temperature-hardness diagram was constructed from the precipitation and coarsening kinetics and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy.展开更多
基金the financial support from the National Natural Science Foundation of China(Grant No.92060102).
文摘Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transformation.Making predictions for TTT diagrams of new steel rapidly and accurately is therefore of much practical importance,especially for costly and time-consuming experimental determination.Here,TTT diagrams for carbon and low-alloy steels were predicted using machine learning methods.Five commonly used machine learning(ML)algorithms,backpropagation artificial neural network(BP network),LibSVM,k-nearest neighbor,Bagging,and Random tree,were adopted to select appropriate models for the prediction.The results illustrate that Bagging is the optimal model for the prediction of pearlite transformation and bainite transformation,and BP network is the optimal model for martensite transformation.Finally,the ML framework composed of Bagging and BP network models was applied to predict the entire TTT diagram.Additionally,the ML models show superior performance on the prediction of testing samples than the commercial software JMatPro.
基金supported by the National Natural Sci-ence Foundation of China(Nos.52050410340 and 51971072)the Fundamental Research Funds for the Central University(No.3072021CFJ1002).
文摘Precipitate hardening is the most easiest and effective way to enhance strain recovery properties in NiTiHf high-temperature shape memory alloys.This paper discusses the precipitation,coarsening and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy during isothermal aging at temperatures between 450℃and 650℃for time to 75 h.The H-phase mean size and volume fraction were determined using transmission electron microscopy.Precipitation kinetics was analyzed using the Johnson-Mehl-Avrami-Kolmogorov equation and an Arrhenius type law.From these analyses,a Time-Temperature-Transformation diagram was constructed.The evolution of H-phase size suggests classical matrix diffusion limited Lifshitz-Slyozov-Wagner coarsening for all considered temperatures.The coarsening rate constants of H-phase precipitation have been determined using a modified coarsening rate equation for nondilute solutions.Critical size of H-phase precipitates for breaking down the precipitate/matrix interface coherency was estimated through a combination of age hardening and precipitate size evolution data.Moreover,time-temperature-hardness diagram was constructed from the precipitation and coarsening kinetics and age hardening of H-phase precipitates in Ni_(50)Ti_(30)Hf_(20)alloy.
